Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality

In this paper, we propose a time-line based framework for topic summarization in Twitter. We summarize topics by sub-topics along time line to fully capture rapid topic evolution in Twitter. Specifically, we rank and select salient and diversified tweets as a summary of each sub-topic. We have observed that ranking tweets is significantly different from ranking sentences in traditional extractive document summarization. We model and formulate the tweet ranking in a unified mutual reinforcement graph, where the social influence of users and the content quality of tweets are taken into consideration simultaneously in a mutually reinforcing manner. Extensive experiments are conducted on 3.9 million tweets. The results show that the proposed approach outperforms previous approaches by 14% improvement on average ROUGE-1. Moreover, we show how the content quality of tweets and the social influence of users effectively improve the performance of measuring the salience of tweets. TITLE AND ABSTRACT IN ANOTHER LANGUAGE (CHINESE) úŽ(7> q͛Œ‡,(τ ̈yݘX ,‡Ðú† *úŽöôt„ ̈yݘê ̈XF¶ ݘ göôz Pݘ Pݘ-9n‡,„́¦Œ 7'ù ̈y‡,ÛL’Œ1⁄2Öå X ì(ø ’ž: þ!‹ ö Q‡,…1 \ > q͛Œ‡,(ÏÛL’ žŒÓœh 1 ,‡Ðú„!‹(úÆ!‹ ROUGE-1sGÐ؆14% 2 \ > q͛Œ‡ ,(Ï H09ۆ‡,́¦„¦Ï